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Prediction of Mechanical Properties of Artificially Weathered Wood by Color Change and Machine Learning Nasir, Vahid; Fathi, Hamidreza; Fallah, Arezoo; Kazemirad, Siavash; Sassani, Farrokh; Antov, Petar

Abstract

Color parameters were used in this study to develop a machine learning model for predicting the mechanical properties of artificially weathered fir, alder, oak, and poplar wood. A CIELAB color measuring system was employed to study the color changes in wood samples. The color parameters were fed into a decision tree model for predicting the MOE and MOR values of the wood samples. The results indicated a reduction in the mechanical properties of the samples, where fir and alder were the most and least degraded wood under weathering conditions, respectively. The mechanical degradation was correlated with the color change, where the most resistant wood to color change exhibited less reduction in the mechanical properties. The predictive machine learning model estimated the MOE and MOR values with a maximum R² of 0.87 and 0.88, respectively. Thus, variations in the color parameters of wood can be considered informative features linked to the mechanical properties of small-sized and clear wood. Further research could study the effectiveness of the model when analyzing large-sized timber.

Item Metadata

Title
Prediction of Mechanical Properties of Artificially Weathered Wood by Color Change and Machine Learning
Creator
Nasir, Vahid; Fathi, Hamidreza; Fallah, Arezoo; Kazemirad, Siavash; Sassani, Farrokh; Antov, Petar
Publisher
Multidisciplinary Digital Publishing Institute
Date Issued
2021-10-22
Description
Color parameters were used in this study to develop a machine learning model for predicting the mechanical properties of artificially weathered fir, alder, oak, and poplar wood. A CIELAB color measuring system was employed to study the color changes in wood samples. The color parameters were fed into a decision tree model for predicting the MOE and MOR values of the wood samples. The results indicated a reduction in the mechanical properties of the samples, where fir and alder were the most and least degraded wood under weathering conditions, respectively. The mechanical degradation was correlated with the color change, where the most resistant wood to color change exhibited less reduction in the mechanical properties. The predictive machine learning model estimated the MOE and MOR values with a maximum R² of 0.87 and 0.88, respectively. Thus, variations in the color parameters of wood can be considered informative features linked to the mechanical properties of small-sized and clear wood. Further research could study the effectiveness of the model when analyzing large-sized timber.
Subject
wood characterization; mechanical properties; photodegradation; artificial weathering; color change; ultraviolet radiation; machine learning
Genre
Article
Type
Text
Language
eng
Date Available
2021-11-23
Provider
Vancouver : University of British Columbia Library
Rights
CC BY 4.0
DOI
10.14288/1.0403806
URI
http://hdl.handle.net/2429/80248
Affiliation
Applied Science, Faculty of; Non UBC; Mechanical Engineering, Department of
Citation
Materials 14 (21): 6314 (2021)
Publisher DOI
10.3390/ma14216314
Peer Review Status
Reviewed
Scholarly Level
Faculty
Rights URI
https://creativecommons.org/licenses/by/4.0/
Aggregated Source Repository
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CC BY 4.0